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MATHEMATICS AND COMPUTERS IN SIMULATION

By Suchendra M. Bh, Arkar A, Jean Koh B and Minsoo Suk B

Abstract

The problem of image segmentation can be formulated as one of vector quantization. Although self-organizing networks with competitive learning are useful for vector quantization, they, in their original single-layer structure, are inadequate for image segmentation. This paper proposes and describes a hierarchical self-organizing neural network for image segmentation. The hierarchical self-organizing feature map (HSOFM) which is an extension of the traditional (single-layer) self-organizing feature map (SOFM) is seen to alleviate the shortcomings of the latter in the context of image segmentation. The problem of image segmentation is formulated as one of vector quantization and mapped onto the HSOFM. The HSOFM combines the ideas of self-organization and topographic mapping with those of multi-scale image segmentation. Experimental results using intensity and range images bring out the advantages of the HSOFM over the conventional SOFM

Topics: Image segmentation, Self-organizing feature map, Neural networks, Computer vision
Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.416.7374
Provided by: CiteSeerX
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